Neighborhood sampling methodologies aim to sub-select neighboring nodes at each layer of the GCN, thus limiting the number of node representations in the forward pass and mitigating the exponential expansion of the GCNs receptive field.
VRGCN [1]} implements a variance reduction technique to reduce the sample size in each layer, which achieves good performance with smaller graphs.
However, it requires to store all the intermediate node embeddings during training, leading to a memory complexity close to full-batch training.
GraphSAGE [2]} learns a set of aggregator functions to gather information from a node's local neighborhood. It then concatenates the outputs of these aggregation functions with each node's own representation at each step of the forward pass.
FastGCN [3]} adopts a Monte Carlo approach to evaluate the GCN's forward pass in practice, which computes each node's hidden representation using a fixed-size, randomly-sampled set of nodes.
LADIES [4]} introduces a layer-conditional approach for node sampling, which encourages node connectivity between layers in contrast to FastGCN [3]}.
